context safety score
A score of 36/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
encoded payload
suspicious base64-like blobs detected in page content
malicious redirect
script/meta redirect patterns detected in page source
cloaking
Page conditionally redirects based on referrer or user-agent
malicious redirect
Page contains a JavaScript setTimeout that unconditionally redirects the visitor via window.location.href after 1 second. The destination is constructed dynamically from document.referrer and cookie values (__js_p_), making the final redirect target opaque and unverifiable at static analysis time. This is a classic cloaking/redirect gate used to send bots and crawlers to a benign page while redirecting real users elsewhere. (location: page.html:36-48 (setTimeout redirect block))
obfuscated code
The get_jhash() function performs a computationally intensive pseudo-random hash loop (1,677,696 iterations with XOR/modulo operations) to derive a value stored in the __jhash_ cookie. This pattern is characteristic of bot-detection or fingerprinting challenges used to gate redirect behavior, obscuring the true redirection logic from automated scanners. (location: page.html:7 (get_jhash function))
obfuscated code
Page body contains only a base64-encoded inline GIF image (data:image/gif;base64,...) as visible content, rendering the page blank to users and automated crawlers alike. All functional behavior is hidden inside JavaScript. This blank-page-with-script pattern is a common cloaking technique. (location: page.html:2 (inline base64 GIF in body div))
hidden content
The page sets meta robots 'noindex, noarchive', instructing search engines not to index or cache the page. Combined with the redirect-on-load behavior, this suppresses forensic evidence and prevents web archive capture of whatever content users are eventually redirected to. (location: page.html:1 (meta name='robots' content='noindex, noarchive'))
social engineering
The script harvests the visitor's User-Agent string (navigator.userAgent) and stores it in the __jua_ cookie, then uses referrer-based fingerprinting to classify traffic source (organic search engines vs referral). This profiling is used to customize redirect behavior per visitor type, a technique employed in traffic distribution systems (TDS) that serve different payloads to different audiences. (location: page.html:43 (document.cookie __jua_ assignment) and page.html:8-9 (get_utm_medium / construct_utm_uri))
curl https://api.brin.sh/domain/tricolor.tvCommon questions teams ask before deciding whether to use this domain in agent workflows.
tricolor.tv currently scores 36/100 with a suspicious verdict and low confidence. The goal is to protect agents from high-risk context before they act on it. Treat this as a decision signal: higher scores suggest lower observed risk, while lower scores mean you should add review or block this domain.
Use the score as a policy threshold: 80–100 is safe, 50–79 is caution, 20–49 is suspicious, and 0–19 is dangerous. Teams often auto-allow safe, require human review for caution/suspicious, and block dangerous.
brin evaluates four dimensions: identity (source trust), behavior (runtime patterns), content (malicious instructions), and graph (relationship risk). Analysis runs in tiers: static signals, deterministic pattern checks, then AI semantic analysis when needed.
Identity checks source trust, behavior checks unusual runtime patterns, content checks for malicious instructions, and graph checks risky relationships to other entities. Looking at sub-scores helps you understand why an entity passed or failed.
brin performs risk assessments on external context before it reaches an AI agent. It scores that context for threats like prompt injection, hijacking, credential harvesting, and supply chain attacks, so teams can decide whether to block, review, or proceed safely.
No. A safe verdict means no significant risk signals were detected in this scan. It is not a formal guarantee; assessments are automated and point-in-time, so combine scores with your own controls and periodic re-checks.
Re-check before high-impact actions such as installs, upgrades, connecting MCP servers, executing remote code, or granting secrets. Use the API in CI or runtime gates so decisions are based on the latest scan.
Learn more in threat detection docs, how scoring works, and the API overview.
Assessments are automated and may contain errors. Findings are risk indicators, not confirmed threats. This is a point-in-time assessment; security posture can change.
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